CS345 Data Mining Web Spam Detection

Economic considerations  Search has become the default gateway to the web  Very high premium to appear on the first page of search results e.g., e-commerce sites advertising-driven sites

What is web spam?  Spamming = any deliberate action solely in order to boost a web page’s position in search engine results, incommensurate with page’s real value  Spam = web pages that are the result of spamming  This is a very broad defintion SEO industry might disagree! SEO = search engine optimization  Approximately 10-15% of web pages are spam

Web Spam Taxonomy  We follow the treatment by Gyongyi and Garcia-Molina [2004]  Boosting techniques Techniques for achieving high relevance/importance for a web page  Hiding techniques Techniques to hide the use of boosting  From humans and web crawlers

Boosting techniques  Term spamming Manipulating the text of web pages in order to appear relevant to queries  Link spamming Creating link structures that boost page rank or hubs and authorities scores

Term Spamming  Repetition of one or a few specific terms e.g., free, cheap, viagra Goal is to subvert TF.IDF ranking schemes  Dumping of a large number of unrelated terms e.g., copy entire dictionaries  Weaving Copy legitimate pages and insert spam terms at random positions  Phrase Stitching Glue together sentences and phrases from different sources

Term spam targets  Body of web page  Title  URL  HTML meta tags  Anchor text

Link spam  Three kinds of web pages from a spammer’s point of view Inaccessible pages Accessible pages  e.g., web log comments pages  spammer can post links to his pages Own pages  Completely controlled by spammer  May span multiple domain names

Link Farms  Spammer’s goal Maximize the page rank of target page t  Technique Get as many links from accessible pages as possible to target page t Construct “link farm” to get page rank multiplier effect

Link Farms Inaccessible t AccessibleOwn 1 2 M One of the most common and effective organizations for a link farm

Analysis Suppose rank contributed by accessible pages = x Let page rank of target page = y Rank of each “farm” page = y/M + (1-)/N y = x + M[y/M + (1-)/N] + (1-)/N = x +  2 y + (1-)M/N + (1-)/N y = x/(1- 2 ) + cM/N where c = /(1+) Inaccessibl e t Accessible Own 1 2 M Very small; ignore

Analysis  y = x/(1- 2 ) + cM/N where c = /(1+)  For  = 0.85, 1/(1- 2 )= 3.6 Multiplier effect for “acquired” page rank By making M large, we can make y as large as we want Inaccessibl e t Accessible Own 1 2 M

Hiding techniques  Content hiding Use same color for text and page background  Cloaking Return different page to crawlers and browsers  Redirection Alternative to cloaking Redirects are followed by browsers but not crawlers

Detecting Spam  Term spamming Analyze text using statistical methods e.g., Naïve Bayes classifiers Similar to spam filtering Also useful: detecting approximate duplicate pages  Link spamming Open research area One approach: TrustRank

TrustRank idea  Basic principle: approximate isolation It is rare for a “good” page to point to a “bad” (spam) page  Sample a set of “seed pages” from the web  Have an oracle (human) identify the good pages and the spam pages in the seed set Expensive task, so must make seed set as small as possible

Trust Propagation  Call the subset of seed pages that are identified as “good” the “trusted pages”  Set trust of each trusted page to 1  Propagate trust through links Each page gets a trust value between 0 and 1 Use a threshold value and mark all pages below the trust threshold as spam

Example good bad

Rules for trust propagation  Trust attenuation The degree of trust conferred by a trusted page decreases with distance  Trust splitting The larger the number of outlinks from a page, the less scrutiny the page author gives each outlink Trust is “split” across outlinks

Simple model  Suppose trust of page p is t(p) Set of outlinks O(p)  For each q 2 O(p), p confers the trust t(p)/|O(p)| for 0<<1  Trust is additive Trust of p is the sum of the trust conferred on p by all its inlinked pages  Note similarity to Topic-Specific Page Rank Within a scaling factor, trust rank = biased page rank with trusted pages as teleport set

Picking the seed set  Two conflicting considerations Human has to inspect each seed page, so seed set must be as small as possible Must ensure every “good page” gets adequate trust rank, so need make all good pages reachable from seed set by short paths

Approaches to picking seed set  Suppose we want to pick a seed set of k pages  PageRank Pick the top k pages by page rank Assume high page rank pages are close to other highly ranked pages We care more about high page rank “good” pages

Inverse page rank  Pick the pages with the maximum number of outlinks  Can make it recursive Pick pages that link to pages with many outlinks  Formalize as “inverse page rank” Construct graph G’ by reversing each edge in web graph G Page Rank in G’ is inverse page rank in G  Pick top k pages by inverse page rank